ArticleiScience2024
Explainable machine learning for predicting 30-day readmission in acute heart failure patients.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024.Frontiers in medicine · 2025Pooled it
- Predicting 30-day readmission after heart failure hospitalization using interpretable machine learning: evidence from a 20-year population-based rural registry.Medical & biological engineering & computing · 2026Article
- Metabolomic Atlas of Cardiovascular Diseases: Mapping Shared and Specific Signatures.JACC. Advances · 2026Article
- Machine learning to optimize the diagnostic performance of natriuretic peptides for acute heart failure across age groups.ESC heart failure · 2026Article
- Construction and validation of a 28-day mortality prediction model for refeeding syndrome in critically ill patients.Frontiers in nutrition · 2026Article
- Development and validation of a deep neural network for predicting coronary heart disease in hypertensive patients using 24-hour ambulatory blood pressure monitoring: a retrospective study.Frontiers in medicine · 2026Article
- Machine learning-based identification of heart failure with reduced ejection fraction using routine laboratory indicators.Frontiers in cardiovascular medicine · 2026Article
- Development and validation of an integrated prognostic model for all-cause mortality in heart failure: a comprehensive analysis combining clinical, electrocardiographic, and echocardiographic parameters.BMC cardiovascular disorders · 2025Article
- Article
- Systemic immune-inflammatory biomarkers combined with the CRP-albumin-lymphocyte index predict surgical site infection following posterior lumbar spinal fusion: a retrospective study using machine learning.Frontiers in medicine · 2025Article
- A w-ACT model for sarcopenia among community-dwelling older adults based on National Basic Public Health Services: development and validation study.Frontiers in public health · 2025Article
- Development and validation of a risk prediction model for 30-day readmission in elderly type 2 diabetes patients complicated with heart failure: a multicenter, retrospective study.Frontiers in endocrinology · 2025Article
- Limited predictive value of traditional comorbidities for readmission in acute decompensated heart failure.PloS one · 2025Article
- Leveraging ensemble learning for predicting 30-day readmission in heart failure ICU patients.Digital healthArticle
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to develop a machine-learning based predictive model to identify 30-day readmission risk in Acute heart failure (AHF) patients. In this study 2232 patients hospitalized with AHF were included. The variance inflation factor value and 5-fold cross-validation were used to select vital clinical variables. Five machine learning algorithms with good performance were applied to develop models, and the discrimination ability was comprehensively evaluated by sensitivity, specificity, and area under the ROC curve (AUC). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values. Finally, the XGBoost model performs optimally: the greatest AUC of 0.763 (0.703-0.824), highest sensitivity of 0.660, and high accuracy of 0.709. This study developed an optimal XGBoost model to predict the risk of 30-day unplanned readmission for AHF patients, which showed more significant performance compared with traditional logistic regression (LR) model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.